from src import heuristics, problems, measure, astar_solution
import matplotlib.pyplot as     plt

n = 11

prob_sets = problems.incrementing_dirt_obs_sets(n, 3)

s_manhatan_data={}

"""
algo = astar_solution.astar_solution(heuristics.scaled_manhattan_dirt(0.5))
s_manhatan_data[0.5] = measureTime.measureTime(prob_sets, algo)
"""

algo = astar_solution.astar_solution(heuristics.scaled_manhattan_dirt(1))
s_manhatan_data[1] = measure.measureTime(prob_sets, algo)

algo = astar_solution.astar_solution(heuristics.scaled_manhattan_dirt(1.5))
s_manhatan_data[1.5] = measure.measureTime(prob_sets, algo)

algo = astar_solution.astar_solution(heuristics.scaled_manhattan_dirt(2.5))
s_manhatan_data[2.5] = measure.measureTime(prob_sets, algo)

algo = astar_solution.astar_solution(heuristics.scaled_manhattan_dirt(3.5))
s_manhatan_data[3.5] = measure.measureTime(prob_sets, algo)

algo = astar_solution.astar_solution(heuristics.scaled_manhattan_dirt(5))
s_manhatan_data[5] = measure.measureTime(prob_sets, algo)

temp = zip(s_manhatan_data.keys(),['r', 'g', 'b', 'y','r--', 'b--'])
#plt.plot(range(n), man_min_data, 'r--', range(n), fast_data, 'b--', range(n), manhatan_data, 'g--')
#plt.plot(range(n), fast_data, 'b--', range(n), manhatan_data, 'r--')



for d,c,l in zip(s_manhatan_data.values(),['r', 'g', 'b', 'y','r--'],s_manhatan_data.keys()):
    plt.plot(range(n), d, c, label= str(l))
"""
plt.plot(range(n),s_manhatan_data[3.5])
"""
plt.legend()
plt.show()


